In the facilities management field, energy performance generally refers to how much energy is being consumed in the operation of a typical building or a specific building. Usually, this does not include special cases where a significant amount of energy is being consumed in the building for energy intensive manufacturing operations, such as an automobile assembly plant or a foundry. Instead, it involves energy as a support operation for use of a facility such as an office building, school system, hospital and the like.
Whenever energy is consumed in a building, there is an interest in determining how that building performs in terms of energy consumption relative to the whole population, or some subset of the population that is similar to that building. The methods that have been used in the past for such comparisons have often been referred to as benchmarking, especially when there is an interest in doing a quick calculation of energy consumption or of providing only a rough feel.
Often, such benchmarking involves calculating the total energy that is consumed in a building or set of buildings, possibly across all energy sources. Thus, it might involve calculating the sum of energy consumed through, for example, electricity, natural gas and fuel oil. Once computed, each of these measures are typically compared to some simple but important metric. For example, metrics that might be used to normalize such benchmark calculations could include the total floor area of the building (e.g., square footage), the number of people in the building, the number of occupants, or in a hospital it might be the number of beds.
Frequently, the way such benchmarking is done is that all the energy consumed in the building is added up, the total energy is converted (if necessary) to common units (e.g., BTUs), and then it is compared to the selected metric (e.g., the floor area or square footage). As one example, the annual energy consumption in a building across all sources may be aggregated and then divided by the square footage of the building to provide a benchmark. In this case, the benchmark many be expressed as the number of BTUs per year per square foot. This type of benchmark is commonly known as an energy use index.
As persons skilled in the art will appreciate, the foregoing methodology for benchmarking the energy use in a building is not without problems. One problem is that electricity is a fuel source that comes into a building at 100% efficiency, whereas natural gas or another fuel is typically not 100% efficient. For this reason, any comparison of energy performance between a building that consumes only electricity versus a building that consumes mostly natural gas and only a small amount of electricity will automatically have some built-in error. This error results from the inefficient nature of natural gas compared to electricity, even though the total energy coming into both buildings may be the same.
In a typical building, where electricity is used for everything (e.g., lighting, heating, cooling, computers, etc.), there will be less energy input to the building than what would be required if fuels were used. For example, if heating and domestic water are accomplished in that same building with natural gas, the energy input into the building will be higher than if electricity were used. Although this may not seem like a big difference, when one considers that the efficiency of such combustion systems is often 80% or less, the differences can be large when accumulated over multiple systems and a sufficiently long period of time.
Oftentimes, what is done to avoid this type of error in the results is that buildings are benchmarked by utility type. Thus, instead of looking at, for example, total BTUs per square foot in terms of aggregate energy use for electricity (after conversion) and natural gas, buildings may be benchmarked in terms of, for example, kilowatt hours per square foot of electrical use separately from BTUs per square foot of natural gas. This results in a more accurate system because it overcomes the disconnect resulting from efficiencies of use inherent in different types of energy use.
This is not to say that a benchmarking methodology that performs separate comparisons based on energy use type is without problems. For example, one problem with such a methodology is that even when two buildings are completely identical (e.g., same construction, same heating and cooling systems, same lighting and loads, etc.), the way in which they consume energy may be completely different due to their geographic locations. For example, identical buildings located in Florida and Alaska would each be expected to use energy in significantly different ways. The reason for this is obvious—it is much harder to cool the building in Florida than in Alaska. Conversely, it is much harder to heat the building in Alaska than in Florida. Of course, there are a wide variety of locations across those descriptions which could result in equally skewed results.
In some benchmarking methodologies, the differences in energy usage resulting from differing geographic locations is essentially ignored. More often, however, what is done to overcome this problem is that buildings are benchmarked across a group of buildings within a particular geographic region that provides a logical basis for comparison. For example, data may be collected and used to compute the total BTUs per square foot or kilowatt hours per square foot across all buildings in Wisconsin. Then, when another building comes along in Green Bay, Wis., it can be compared to the other buildings in Wisconsin with a reasonable level of comfort that they are basically similar.
Although the foregoing is a fairly good system for benchmarking energy use, it also has some problems. One problem is that the reliability or accuracy of one's ability to benchmark (e.g., determine where one particular building stands in terms of energy consumption relative to all other buildings in the region, or to all buildings in the region having a similar construction or use) is dependent upon the population (i.e., the number of buildings for which there is data). Thus, if there is data for only one building and a second building is brought in and compared to the first building, the basis of comparison is relatively poor because it is unknown whether the first building is average. For this reason, it is desirable to have a population that is as large as possible. When the benchmarking methodology is restricted to only certain local climate geographies, the ability to have a large population is limited.
In view of the foregoing, it would be beneficial to provide methods and systems for comparing buildings regardless of their climate, but still obtain accurate results. Moreover, it would be desirable to provide methods and systems that allow accurate benchmarks to be computed regardless of where the buildings are located. It would further be desirable to provide methods and systems that allow the collection of data from buildings all around the world and to compare the performance of any one building to that entire set and be confident in the results. It would further be desirable to provide methods and systems that allow the construction of large databases of benchmarking data from widely geographically dispersed buildings.